Project Intern – AI Security Engineering

Job Description

Machine Learning Engineer Project Intern (Security-Data) | ByteDance

The Tone:
This is a project internship at ByteDance, a company founded in 2012 that builds products like TikTok, Lemon8, and CapCut, striving to inspire creativity and enrich life. The security engineering team is dedicated to building robust services and platforms to protect users, products, and infrastructure. This role is crucial for advancing the company’s security initiatives by enhancing the intelligence of large models and applying them to critical security tasks.

The TL;DR
• Role: Internship
• Type: Intern
• Location: US-based
• Pay: $45 hourly
• Team: Security Engineering Team
• Mission: Enhance and apply large language models to critical security tasks such as vulnerability detection and threat intelligence.
• Tech Stack: C++, Java, Python, Hive, SQL, Spark, Flink, machine learning platforms, deep learning platforms

What You’ll Actually Do
• Model Improvement: Improve the intelligent upper limit of large models specifically within the security domain.
• Model Application: Train vertical large models and apply them to various security tasks, including code analysis, vulnerability detection, and threat intelligence.
• Technology Innovation: Deeply apply and innovate large model technology stacks, such as data synthesis, model post-training, and model acceleration.
• Technical Exploration: Explore the technical boundaries and new possibilities within large model technology.

The Must-Haves
• Background: Undergraduate or Postgraduate student pursuing a degree/master in Computer Science, Computer Engineering, Security, Statistics, Artificial Intelligence, or other STEM disciplines.
• Experience: Proven experience with C++, Java, or Python, familiar with at least one common machine learning or deep learning platform. Proven skills with common data mining algorithms and theories. Proven experience in big data processing, proficient in using Hive, SQL, Spark, and Flink.
• Skills: Excellent coding ability, understanding of data mining algorithms (data denoising, statistical learning, classification and clustering, anomaly detection, graph learning), proficiency in big data analysis and mining tools.
• Bonus: Ability to implement machine learning algorithms with practical experience, self-drive, fast learning ability, good communication skills, and innovative thinking.